Meta Llama
Open-weight frontier models
0 case studies
1 specialists
3 specialists
General Foundation Model Open-weight frontier models
Running open-weight large language models locally or on your own infrastructure for full control over data privacy, fine-tuning, and deployment costs. Llama models are widely used for self-hosted chatbots, custom fine-tuned applications, and research where model transparency is required.
Download model weights from llama.meta.com or access them via Hugging Face (huggingface.co/meta-llama). Run locally with tools like llama.cpp, vLLM, or Ollama, or use hosted endpoints from Together AI, Replicate, or Groq for instant API access without managing infrastructure.
Be the first to share a Meta Llama case study and get discovered by clients.
Submit a case studyThought leaders
Follow for insights, tutorials, and thought leadership
Hugging Face
Head of Global Policy at Hugging Face, leading AI policy and governance for the world's largest open-source AI platform. Expert in responsible AI deployment, open-source governance frameworks, and societal risk mitigation.
Lightning AI
LLM Research Engineer at Lightning AI and statistics professor at University of Wisconsin-Madison. Author of 'Build a Large Language Model From Scratch' and the 'Ahead of AI' magazine. Created LitGPT, an open-source library for pre-training, fine-tuning, and deploying LLMs. His 'Python Machine Learning' book series has been translated into many languages. PhD in Computational Biology.
Meta (FAIR Lab)
VP of AI Research at Meta leading the Fundamental AI Research (FAIR) lab. Driven breakthroughs in reinforcement learning, robotics, and open-access AI models including Llama. McGill University professor. Advocate for reproducibility in AI research and open-source model development.
Submit a brief and we'll match you with vetted specialists who have proven Meta Llama experience.
